Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations40541
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.2 MiB
Average record size in memory341.5 B

Variable types

Text4
Numeric6
DateTime1

Alerts

latitude is highly overall correlated with temperature_celsiusHigh correlation
temperature_celsius is highly overall correlated with latitudeHigh correlation
precip_mm is highly skewed (γ1 = 21.67108377) Skewed
precip_mm has 27994 (69.1%) zeros Zeros

Reproduction

Analysis started2025-04-05 12:58:29.078220
Analysis finished2025-04-05 12:58:33.407930
Duration4.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Distinct185
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-04-05T13:58:33.778609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length8.6122197
Min length4

Characters and Unicode

Total characters349148
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAndorra
5th rowAngola
ValueCountFrequency (%)
and 1040
 
2.1%
islands 832
 
1.6%
republic 832
 
1.6%
united 624
 
1.2%
guinea 624
 
1.2%
saint 624
 
1.2%
bulgaria 560
 
1.1%
indonesia 426
 
0.8%
south 416
 
0.8%
of 416
 
0.8%
Other values (202) 44129
87.3%
2025-04-05T13:58:34.255593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 55462
15.9%
i 31264
 
9.0%
n 28268
 
8.1%
e 23378
 
6.7%
r 19595
 
5.6%
o 16472
 
4.7%
u 13981
 
4.0%
l 13081
 
3.7%
t 12908
 
3.7%
s 12102
 
3.5%
Other values (42) 122637
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289499
82.9%
Uppercase Letter 49263
 
14.1%
Space Separator 9982
 
2.9%
Dash Punctuation 404
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 55462
19.2%
i 31264
10.8%
n 28268
9.8%
e 23378
 
8.1%
r 19595
 
6.8%
o 16472
 
5.7%
u 13981
 
4.8%
l 13081
 
4.5%
t 12908
 
4.5%
s 12102
 
4.2%
Other values (16) 62988
21.8%
Uppercase Letter
ValueCountFrequency (%)
S 5896
12.0%
B 4692
 
9.5%
M 4158
 
8.4%
C 3536
 
7.2%
A 3328
 
6.8%
G 2913
 
5.9%
I 2796
 
5.7%
T 2689
 
5.5%
N 2284
 
4.6%
L 2277
 
4.6%
Other values (14) 14694
29.8%
Space Separator
ValueCountFrequency (%)
9982
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 338762
97.0%
Common 10386
 
3.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 55462
16.4%
i 31264
 
9.2%
n 28268
 
8.3%
e 23378
 
6.9%
r 19595
 
5.8%
o 16472
 
4.9%
u 13981
 
4.1%
l 13081
 
3.9%
t 12908
 
3.8%
s 12102
 
3.6%
Other values (40) 112251
33.1%
Common
ValueCountFrequency (%)
9982
96.1%
- 404
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 349148
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 55462
15.9%
i 31264
 
9.0%
n 28268
 
8.1%
e 23378
 
6.7%
r 19595
 
5.6%
o 16472
 
4.7%
u 13981
 
4.0%
l 13081
 
3.7%
t 12908
 
3.7%
s 12102
 
3.5%
Other values (42) 122637
35.1%
Distinct220
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-04-05T13:58:34.624022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length15
Mean length7.6521053
Min length3

Characters and Unicode

Total characters310224
Distinct characters54
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKabul
2nd rowTirana
3rd rowAlgiers
4th rowAndorra La Vella
5th rowLuanda
ValueCountFrequency (%)
city 1040
 
2.2%
port 832
 
1.8%
san 624
 
1.3%
saint 416
 
0.9%
beirut 209
 
0.4%
tbilisi 209
 
0.4%
vienna 208
 
0.4%
baku 208
 
0.4%
dhaka 208
 
0.4%
gaborone 208
 
0.4%
Other values (231) 42422
91.1%
2025-04-05T13:58:35.048902image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 46563
15.0%
o 22382
 
7.2%
i 21582
 
7.0%
n 20478
 
6.6%
r 18849
 
6.1%
e 17231
 
5.6%
u 13742
 
4.4%
s 12501
 
4.0%
t 12266
 
4.0%
l 10448
 
3.4%
Other values (44) 114182
36.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 254297
82.0%
Uppercase Letter 47832
 
15.4%
Space Separator 6043
 
1.9%
Dash Punctuation 1040
 
0.3%
Other Punctuation 804
 
0.3%
Modifier Symbol 208
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 46563
18.3%
o 22382
 
8.8%
i 21582
 
8.5%
n 20478
 
8.1%
r 18849
 
7.4%
e 17231
 
6.8%
u 13742
 
5.4%
s 12501
 
4.9%
t 12266
 
4.8%
l 10448
 
4.1%
Other values (15) 58255
22.9%
Uppercase Letter
ValueCountFrequency (%)
B 5807
12.1%
M 4188
 
8.8%
S 4160
 
8.7%
P 4121
 
8.6%
A 4093
 
8.6%
C 3065
 
6.4%
L 2921
 
6.1%
D 2521
 
5.3%
N 2495
 
5.2%
K 2195
 
4.6%
Other values (15) 12266
25.6%
Space Separator
ValueCountFrequency (%)
6043
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1040
100.0%
Other Punctuation
ValueCountFrequency (%)
' 804
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 208
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 302129
97.4%
Common 8095
 
2.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 46563
15.4%
o 22382
 
7.4%
i 21582
 
7.1%
n 20478
 
6.8%
r 18849
 
6.2%
e 17231
 
5.7%
u 13742
 
4.5%
s 12501
 
4.1%
t 12266
 
4.1%
l 10448
 
3.5%
Other values (40) 106087
35.1%
Common
ValueCountFrequency (%)
6043
74.7%
- 1040
 
12.8%
' 804
 
9.9%
` 208
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 310224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 46563
15.0%
o 22382
 
7.2%
i 21582
 
7.0%
n 20478
 
6.6%
r 18849
 
6.1%
e 17231
 
5.6%
u 13742
 
4.4%
s 12501
 
4.0%
t 12266
 
4.0%
l 10448
 
3.4%
Other values (44) 114182
36.8%

latitude
Real number (ℝ)

High correlation 

Distinct216
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.298092
Minimum-41.3
Maximum64.1
Zeros0
Zeros (%)0.0%
Negative8909
Negative (%)22.0%
Memory size316.9 KiB
2025-04-05T13:58:35.171810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-41.3
5-th percentile-24.65
Q13.75
median17.25
Q341.32
95-th percentile53.9
Maximum64.1
Range105.4
Interquartile range (IQR)37.57

Descriptive statistics

Standard deviation24.52135
Coefficient of variation (CV)1.2706619
Kurtosis-0.76273504
Mean19.298092
Median Absolute Deviation (MAD)20.63
Skewness-0.30646662
Sum782363.95
Variance601.29658
MonotonicityNot monotonic
2025-04-05T13:58:35.280604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39.93 416
 
1.0%
41.9 416
 
1.0%
12.15 415
 
1.0%
40.4 414
 
1.0%
41.73 209
 
0.5%
33.87 209
 
0.5%
42 208
 
0.5%
-25.97 208
 
0.5%
-22.57 208
 
0.5%
39.55 208
 
0.5%
Other values (206) 37630
92.8%
ValueCountFrequency (%)
-41.3 207
0.5%
-35.28 208
0.5%
-34.86 208
0.5%
-34.59 208
0.5%
-33.45 208
0.5%
-29.32 208
0.5%
-26.32 182
0.4%
-25.97 208
0.5%
-25.75 208
0.5%
-24.65 208
0.5%
ValueCountFrequency (%)
64.1 25
 
0.1%
63.83 183
0.5%
60.18 208
0.5%
59.92 208
0.5%
59.43 208
0.5%
59.33 208
0.5%
56.95 208
0.5%
55.75 208
0.5%
55.67 208
0.5%
54.68 208
0.5%

longitude
Real number (ℝ)

Distinct217
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.759817
Minimum-175.2
Maximum179.22
Zeros0
Zeros (%)0.0%
Negative11252
Negative (%)27.8%
Memory size316.9 KiB
2025-04-05T13:58:35.380309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-175.2
5-th percentile-84.08
Q1-6.84
median23.24
Q349.88
95-th percentile147.19
Maximum179.22
Range354.42
Interquartile range (IQR)56.72

Descriptive statistics

Standard deviation65.682563
Coefficient of variation (CV)3.0185255
Kurtosis0.34382658
Mean21.759817
Median Absolute Deviation (MAD)28.09
Skewness0.011654161
Sum882164.74
Variance4314.1991
MonotonicityNot monotonic
2025-04-05T13:58:35.518311image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.51 416
 
1.0%
12.45 416
 
1.0%
-58.17 416
 
1.0%
44.79 209
 
0.5%
35.51 209
 
0.5%
4.89 208
 
0.5%
19.26 208
 
0.5%
-6.84 208
 
0.5%
32.59 208
 
0.5%
17.08 208
 
0.5%
Other values (207) 37835
93.3%
ValueCountFrequency (%)
-175.2 208
0.5%
-171.73 206
0.5%
-123.04 21
 
0.1%
-120.49 187
0.5%
-99.13 208
0.5%
-90.53 208
0.5%
-89.2 208
0.5%
-88.77 208
0.5%
-87.22 208
0.5%
-86.27 207
0.5%
ValueCountFrequency (%)
179.22 207
0.5%
178.42 208
0.5%
174.78 207
0.5%
171.38 208
0.5%
169.53 206
0.5%
168.32 208
0.5%
159.95 208
0.5%
158.15 208
0.5%
149.22 208
0.5%
147.19 208
0.5%
Distinct6021
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size316.9 KiB
Minimum2023-08-29 02:45:00
Maximum2024-03-29 05:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-05T13:58:35.643165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:35.755063image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

temperature_celsius
Real number (ℝ)

High correlation 

Distinct628
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.193458
Minimum-41.9
Maximum45.4
Zeros359
Zeros (%)0.9%
Negative1932
Negative (%)4.8%
Memory size316.9 KiB
2025-04-05T13:58:35.867490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-41.9
5-th percentile0
Q112
median22
Q327
95-th percentile32
Maximum45.4
Range87.3
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.71348
Coefficient of variation (CV)0.55818394
Kurtosis0.98066486
Mean19.193458
Median Absolute Deviation (MAD)7
Skewness-0.91195662
Sum778122
Variance114.77866
MonotonicityNot monotonic
2025-04-05T13:58:35.967114image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 1906
 
4.7%
26 1800
 
4.4%
28 1780
 
4.4%
29 1655
 
4.1%
25 1616
 
4.0%
30 1436
 
3.5%
24 1050
 
2.6%
31 988
 
2.4%
23 859
 
2.1%
14 832
 
2.1%
Other values (618) 26619
65.7%
ValueCountFrequency (%)
-41.9 1
 
< 0.1%
-39.4 1
 
< 0.1%
-39 1
 
< 0.1%
-38.3 1
 
< 0.1%
-38 2
< 0.1%
-37.6 1
 
< 0.1%
-37 1
 
< 0.1%
-36 4
< 0.1%
-35.7 1
 
< 0.1%
-35.6 1
 
< 0.1%
ValueCountFrequency (%)
45.4 1
 
< 0.1%
45 1
 
< 0.1%
44.9 1
 
< 0.1%
44.3 1
 
< 0.1%
44 3
< 0.1%
43.9 1
 
< 0.1%
43.8 1
 
< 0.1%
43.6 1
 
< 0.1%
43.3 1
 
< 0.1%
43.2 1
 
< 0.1%

precip_mm
Real number (ℝ)

Skewed  Zeros 

Distinct435
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13340643
Minimum0
Maximum39.64
Zeros27994
Zeros (%)69.1%
Negative0
Negative (%)0.0%
Memory size316.9 KiB
2025-04-05T13:58:36.065551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.77
Maximum39.64
Range39.64
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.6168834
Coefficient of variation (CV)4.6240906
Kurtosis891.5391
Mean0.13340643
Median Absolute Deviation (MAD)0
Skewness21.671084
Sum5408.43
Variance0.38054513
MonotonicityNot monotonic
2025-04-05T13:58:36.174374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27994
69.1%
0.01 1925
 
4.7%
0.02 1026
 
2.5%
0.03 688
 
1.7%
0.04 504
 
1.2%
0.1 439
 
1.1%
0.05 417
 
1.0%
0.06 340
 
0.8%
0.07 324
 
0.8%
0.08 271
 
0.7%
Other values (425) 6613
 
16.3%
ValueCountFrequency (%)
0 27994
69.1%
0.01 1925
 
4.7%
0.02 1026
 
2.5%
0.03 688
 
1.7%
0.04 504
 
1.2%
0.05 417
 
1.0%
0.06 340
 
0.8%
0.07 324
 
0.8%
0.08 271
 
0.7%
0.09 247
 
0.6%
ValueCountFrequency (%)
39.64 1
< 0.1%
31 1
< 0.1%
28.7 1
< 0.1%
21.68 1
< 0.1%
19.6 1
< 0.1%
18.63 1
< 0.1%
17.73 1
< 0.1%
17.7 1
< 0.1%
17.69 1
< 0.1%
15.07 1
< 0.1%

humidity
Real number (ℝ)

Distinct98
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.265805
Minimum3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size316.9 KiB
2025-04-05T13:58:36.283182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile27
Q159
median75
Q387
95-th percentile97
Maximum100
Range97
Interquartile range (IQR)28

Descriptive statistics

Standard deviation21.085619
Coefficient of variation (CV)0.30008365
Kurtosis0.30464366
Mean70.265805
Median Absolute Deviation (MAD)13
Skewness-0.89058337
Sum2848646
Variance444.60335
MonotonicityNot monotonic
2025-04-05T13:58:36.384989image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 2519
 
6.2%
100 1816
 
4.5%
79 1552
 
3.8%
89 1431
 
3.5%
84 1425
 
3.5%
93 1386
 
3.4%
75 1234
 
3.0%
70 1146
 
2.8%
87 1099
 
2.7%
88 1072
 
2.6%
Other values (88) 25861
63.8%
ValueCountFrequency (%)
3 4
 
< 0.1%
4 22
 
0.1%
5 37
0.1%
6 62
0.2%
7 53
0.1%
8 66
0.2%
9 79
0.2%
10 54
0.1%
11 73
0.2%
12 89
0.2%
ValueCountFrequency (%)
100 1816
4.5%
99 45
 
0.1%
98 104
 
0.3%
97 118
 
0.3%
96 113
 
0.3%
95 123
 
0.3%
94 2519
6.2%
93 1386
3.4%
92 225
 
0.6%
91 155
 
0.4%

air_quality_PM2.5
Real number (ℝ)

Distinct2260
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.23774
Minimum0.5
Maximum1558.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size316.9 KiB
2025-04-05T13:58:36.482383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.6
Q12.5
median7.5
Q323
95-th percentile97
Maximum1558.8
Range1558.3
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation63.649937
Coefficient of variation (CV)2.5220142
Kurtosis104.84917
Mean25.23774
Median Absolute Deviation (MAD)6.2
Skewness8.5194913
Sum1023163.2
Variance4051.3145
MonotonicityNot monotonic
2025-04-05T13:58:36.739954image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 1941
 
4.8%
0.9 479
 
1.2%
1 478
 
1.2%
1.1 447
 
1.1%
0.8 442
 
1.1%
1.3 430
 
1.1%
1.5 430
 
1.1%
0.6 428
 
1.1%
1.2 427
 
1.1%
0.7 427
 
1.1%
Other values (2250) 34612
85.4%
ValueCountFrequency (%)
0.5 1941
4.8%
0.6 428
 
1.1%
0.7 427
 
1.1%
0.8 442
 
1.1%
0.9 479
 
1.2%
1 478
 
1.2%
1.1 447
 
1.1%
1.2 427
 
1.1%
1.3 430
 
1.1%
1.4 424
 
1.0%
ValueCountFrequency (%)
1558.8 1
< 0.1%
1329.2 1
< 0.1%
1253.9 1
< 0.1%
1233 1
< 0.1%
1199.1 1
< 0.1%
1179.3 1
< 0.1%
1163.5 1
< 0.1%
1160.2 1
< 0.1%
1146.7 1
< 0.1%
1133 1
< 0.1%
Distinct185
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-04-05T13:58:37.082454image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length32
Median length24
Mean length8.6022545
Min length4

Characters and Unicode

Total characters348744
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowafghanistan
2nd rowalbania
3rd rowalgeria
4th rowandorra
5th rowangola
ValueCountFrequency (%)
and 1040
 
2.1%
islands 832
 
1.6%
republic 832
 
1.6%
united 624
 
1.2%
guinea 624
 
1.2%
saint 624
 
1.2%
bulgaria 560
 
1.1%
indonesia 426
 
0.8%
south 416
 
0.8%
of 416
 
0.8%
Other values (202) 44129
87.3%
2025-04-05T13:58:37.534131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 58790
16.9%
i 34060
 
9.8%
n 30552
 
8.8%
e 25042
 
7.2%
r 21384
 
6.1%
s 17998
 
5.2%
o 16680
 
4.8%
t 15597
 
4.5%
u 15437
 
4.4%
l 15358
 
4.4%
Other values (17) 97846
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 338762
97.1%
Space Separator 9982
 
2.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 58790
17.4%
i 34060
 
10.1%
n 30552
 
9.0%
e 25042
 
7.4%
r 21384
 
6.3%
s 17998
 
5.3%
o 16680
 
4.9%
t 15597
 
4.6%
u 15437
 
4.6%
l 15358
 
4.5%
Other values (16) 87864
25.9%
Space Separator
ValueCountFrequency (%)
9982
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 338762
97.1%
Common 9982
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 58790
17.4%
i 34060
 
10.1%
n 30552
 
9.0%
e 25042
 
7.4%
r 21384
 
6.3%
s 17998
 
5.3%
o 16680
 
4.9%
t 15597
 
4.6%
u 15437
 
4.6%
l 15358
 
4.5%
Other values (16) 87864
25.9%
Common
ValueCountFrequency (%)
9982
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 58790
16.9%
i 34060
 
9.8%
n 30552
 
8.8%
e 25042
 
7.2%
r 21384
 
6.1%
s 17998
 
5.2%
o 16680
 
4.8%
t 15597
 
4.5%
u 15437
 
4.4%
l 15358
 
4.4%
Other values (17) 97846
28.1%
Distinct220
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
2025-04-05T13:58:37.858179image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length19
Median length15
Mean length7.6014898
Min length3

Characters and Unicode

Total characters308172
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkabul
2nd rowtirana
3rd rowalgiers
4th rowandorra la vella
5th rowluanda
ValueCountFrequency (%)
city 1040
 
2.2%
port 832
 
1.8%
san 624
 
1.3%
saint 416
 
0.9%
beirut 209
 
0.4%
tbilisi 209
 
0.4%
vienna 208
 
0.4%
baku 208
 
0.4%
dhaka 208
 
0.4%
gaborone 208
 
0.4%
Other values (231) 42422
91.1%
2025-04-05T13:58:38.315094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 50656
16.4%
o 23214
 
7.5%
n 22973
 
7.5%
i 22026
 
7.1%
r 19934
 
6.5%
e 17356
 
5.6%
s 16661
 
5.4%
t 14361
 
4.7%
u 13948
 
4.5%
l 13369
 
4.3%
Other values (17) 93674
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 302129
98.0%
Space Separator 6043
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 50656
16.8%
o 23214
 
7.7%
n 22973
 
7.6%
i 22026
 
7.3%
r 19934
 
6.6%
e 17356
 
5.7%
s 16661
 
5.5%
t 14361
 
4.8%
u 13948
 
4.6%
l 13369
 
4.4%
Other values (16) 87631
29.0%
Space Separator
ValueCountFrequency (%)
6043
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 302129
98.0%
Common 6043
 
2.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 50656
16.8%
o 23214
 
7.7%
n 22973
 
7.6%
i 22026
 
7.3%
r 19934
 
6.6%
e 17356
 
5.7%
s 16661
 
5.5%
t 14361
 
4.8%
u 13948
 
4.6%
l 13369
 
4.4%
Other values (16) 87631
29.0%
Common
ValueCountFrequency (%)
6043
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 308172
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 50656
16.4%
o 23214
 
7.5%
n 22973
 
7.5%
i 22026
 
7.1%
r 19934
 
6.5%
e 17356
 
5.6%
s 16661
 
5.4%
t 14361
 
4.7%
u 13948
 
4.5%
l 13369
 
4.3%
Other values (17) 93674
30.4%

Interactions

2025-04-05T13:58:32.580896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:29.876040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.368935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.963054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.587995image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.115116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.661333image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:29.969555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.480482image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.053133image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.668513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.187826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.744955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.056105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.558262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.147359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.755040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.267653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.823956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.129117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.636771image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.324903image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.834440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.339233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.916759image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.211630image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.749404image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.419460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.933420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.422603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.999439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.284416image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:30.857441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:31.502996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.023968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-05T13:58:32.496306image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-05T13:58:38.391622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
air_quality_PM2.5humiditylatitudelongitudeprecip_mmtemperature_celsius
air_quality_PM2.51.000-0.1760.0550.180-0.2820.002
humidity-0.1761.0000.0940.2000.328-0.277
latitude0.0550.0941.000-0.051-0.113-0.638
longitude0.1800.200-0.0511.000-0.033-0.229
precip_mm-0.2820.328-0.113-0.0331.0000.033
temperature_celsius0.002-0.277-0.638-0.2290.0331.000

Missing values

2025-04-05T13:58:33.133160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-05T13:58:33.298840image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

countrylocation_namelatitudelongitudelast_updatedtemperature_celsiusprecip_mmhumidityair_quality_PM2.5country_normalizedCity_normalized
0AfghanistanKabul34.5269.182023-08-29 14:0028.80.0197.9afghanistankabul
1AlbaniaTirana41.3319.822023-08-29 11:3027.00.05428.2albaniatirana
2AlgeriaAlgiers36.763.052023-08-29 10:3028.00.0306.4algeriaalgiers
3AndorraAndorra La Vella42.501.522023-08-29 11:3010.20.0510.5andorraandorra la vella
4AngolaLuanda-8.8413.232023-08-29 10:3025.00.069139.6angolaluanda
5Antigua and BarbudaSaint John's17.12-61.852023-08-29 05:3029.00.3790.8antigua and barbudasaint johns
6ArgentinaBuenos Aires-34.59-58.672023-08-29 06:309.00.0712.1argentinabuenos aires
7ArmeniaYerevan40.1844.512023-08-29 13:3031.00.0265.0armeniayerevan
8AustraliaCanberra-35.28149.222023-08-29 19:3013.00.0624.0australiacanberra
9AustriaVienna48.2016.372023-08-29 11:3016.00.08213.1austriavienna
countrylocation_namelatitudelongitudelast_updatedtemperature_celsiusprecip_mmhumidityair_quality_PM2.5country_normalizedCity_normalized
40531United KingdomLondon51.52-0.112024-03-28 16:0010.00.07820.9united kingdomlondon
40532United States of AmericaWashington Park46.60-120.492024-03-28 09:003.30.00824.5united states of americawashington park
40533UruguayMontevideo-34.86-56.172024-03-28 13:0024.00.00546.2uruguaymontevideo
40534UzbekistanTashkent41.3269.252024-03-28 21:009.00.008111.6uzbekistantashkent
40535VanuatuPort Vila-17.73168.322024-03-29 03:0025.00.621003.7vanuatuport vila
40536VenezuelaCaracas10.50-66.922024-03-28 12:0028.30.00391.9venezuelacaracas
40537VietnamHanoi21.03105.852024-03-28 23:0025.00.009478.6vietnamhanoi
40538YemenSanaa15.3544.212024-03-28 19:0019.60.45515.5yemensanaa
40539ZambiaLusaka-15.4228.282024-03-28 18:0025.00.005813.0zambialusaka
40540ZimbabweHarare-17.8231.042024-03-28 18:0024.40.004525.1zimbabweharare